Comparative Economic Studies, 2016, 58, (254–278)

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Article

Labor-Market Volatility and Financial Development in the Advanced OECD Countries: Does Labor-Market Regulation Matter? THIBAULT DARCILLON Maison des Sciences Economiques, University of Paris I Panthéon-Sorbonne, 106-112 Boulevard de l’Hôpital, Paris 75013, France. E-mail: [email protected]

This paper investigates the relationship between financial development and labormarket volatility in 15 OECD countries from 1974 to 2007. I argue that financial development should affect corporate governance and then how firms will determine wages and the number of hours worked, especially for low-skilled workers. First, my results indicate that financial development is associated with higher employment and wage volatility, but with no significant differences across skill levels. Second, using a threshold regression model, I show that the increasing effect of higher financial development on labor-market volatility is larger in countries with more labor-market regulation. Comparative Economic Studies (2016) 58, 254–278. doi:10.1057/ces.2016.2; published online 4 February 2016

Keywords: financial development, labor-market volatility, labor-market regulation, social and welfare policies, threshold regression model JEL Classification: G1, I39, J63

INTRODUCTION Since the 1980s economic insecurity has grown, especially for low-skilled workers, with higher unemployment rates, less stable employment and more volatile wages (OECD, 2007). At the same time, most advanced OECD

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economies also have experienced a sharp increase in their financial activities over the last three decades: financial markets, financial institutions and financial innovations have been rapidly grown across countries since the early 1980s. A large literature has explored the potential impact of the international economic integration on the increased uncertainty faced by workers (Rodrik, 1997; Buch and Schlotter, 2011). For instance, Rodrik (1997) shows that the international economic integration has contributed to the increase in the elasticity in labor demand, especially for low-skilled workers, making wages and the number of hours worked more volatile. The aim of this paper is to analyze the relationship between financial development and labor-market volatility in the OECD countries. Some recent contributions have investigated the role of increasing financialization in higher labor-market volatility. Pagano and Pica (2012) have analyzed the impact of financial development on labor reallocation across industries. More specifically, Buch and Pierdzioch (2014) have investigated on the role of financial globalization in labor-market volatility. This paper focuses on the relationship between domestic financial development (and its impact of corporate strategies) and labor-market volatility. To analyze the impact of financial development on labor-market volatility, I argue that higher financial development should affect corporate strategies and be positively correlated to labor-market volatility. Thesmar and Thoenig (2004) argue that financial development has directly affected corporate strategies. For instance, shareholder-oriented firms are likely to become more sensitive to the financial market fluctuations. Increasing financial development has reinforced investors’ bargaining power at the global and domestic levels, pushing for pro-minority shareholder corporate governance reforms in most OECD countries (Darcillon, 2015). As a consequence, recent changes in corporate governance thus have contributed to the shift of risk from shareholders to wage earners. This will then affect how firms will determine wages and the number of hours worked. However, there are large cross-country differences in labor-market volatility, whereas all OECD countries have experienced a large increase in their financial activities. As the result, the increase in labor-market volatility has been more modest in countries that have maintained strong labor-market regulation. In this regard, it has been well recognized in the literature that specific labor-market regulations (such unemployment benefits or job-protection laws) are designed to reduce temporary fluctuations of income (Jetter et al., 2013; Bertola and Lo Prete, 2015). Individuals’ preferences on stable wages and employment are closely related to risk aversion. I will test the argument that financial development should be positively correlated to higher labor-market volatility in countries with weaker labor-market regulation. In this paper, labor-market regulation Comparative Economic Studies

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refers to two dimensions: first, the level of government in the labor market; second, the degree of generosity of social and welfare policies. Using panel data on 15 OECD countries from 1974 to 2007, I first run fixed-effects regressions to analyze the effect of higher financial development on labor-market volatility. Then, I estimate a threshold regression model in panel data using a reduced sample of countries from 1986 to 2005, proposed by Hansen (1999), to test whether this effect depends on labor-market regulation. I measure labor-market instability using indices of volatility. I calculate how much wages and the number of hours worked change over a 5-year window. The greater the variance of short-term wages and employment changes, the more volatility there is. I use two measures of financial development: the stock market capitalization ratio (as a percentage of GDP) and the employment share in the financial sector. I find evidence that higher employment in finance is positively correlated to increased labor-market volatility, whereas stock market development has no significant impact. Results do not, however, show any significant differences across skill levels. Then, results from threshold regressions indicate that this positive effect is larger particularly in countries with weaker labor-market regulation (which includes less generous social and welfare policies). This second result is particularly more robust when explaining employment volatility of lowskilled workers and when explaining wage volatility for all categories of workers. The next section of the paper presents my conceptual framework. In the subsequent section, I present data on recent trends in labor-market volatility and detail my measures of financial development and the indicators for labor-market regulation. Then, I explain my empirical strategy in the section after that. Estimation results and some robustness checks are reported in the penultimate section. The final section provides some concluding remarks and policy implications.

CONCEPTUAL FRAMEWORK Financial development and labor-market volatility The aim of my paper is to analyze the relationship between financial development and labor-market volatility. To do so, I rely on some recent theoretical and empirical contributions examining how an increasing influence of the financial markets is likely to be associated with higher labor-market volatility. I focus on two different theoretical mechanisms to which financial development may be connected to higher labor-market volatility: (1) the capital ownership structure will impact the allocation of risk between workers Comparative Economic Studies

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and shareholders and (2) the use of pay incentive schemes induced by financial development should be related to higher demand volatility. First, in line with Jacoby (2008), I focus on corporate governance to explain how financial development is correlated to higher labor-market volatility. In this regard, the capital ownership structure will impact the allocation of risk between workers, which is strongly related to labor-market volatility. Corporate governance refers to the ‘private rules by which owners, managers, and workers influence a firm’s strategic decisions, including the distribution of rents and risk’ (Jacoby, 2008, p. 8). Jacoby (2008) argues that the relation between financial and labor markets is shaped by regulation and corporate governance. I argue that any change in corporate governance is likely to be related to higher labor-market volatility. An increasing influence of the financial markets should affect the corporate strategy by altering the allocation of risks between owners and corporate stakeholders (including creditors, suppliers or employees) and then should affect how firms will determine how wages and the number of hours worked are set. As underlined in a large literature (Roe, 2003; Pagano and Volpin, 2005), labor-market regulation is strongly related to the structure of capital ownership. The allocation of risk between owners and stakeholders is largely determined by the capital ownership structure, and this because blockholders (ie, large shareholders with at least 5% of shares) and minority shareholders may have opposed risk preferences. In the post-war era, corporations in most OECD countries were owned by blockholders. During this era, workers accepted to cede authority to managers in exchange for stable jobs and pay increase (Gourevitch and Shinn, 2005; Darcillon, 2015). Roe (2003) shows that ‘blockholding’ is associated with strong regulation in the economy that creates higher incentives to workers to invest in specific human capital. Then, during the 1970s and the 1980s, financial markets were liberalized and capital controls were abolished in most OECD countries: all the advanced OECD countries have since then adopted reforms intended to strengthen the power of minority shareholders within firms (Darcillon, 2015). Accordingly, financial development and recent changes in corporate governance have contributed to the shift of risk from shareholders to wage earners. Prominority shareholder reforms increase short-term corporate profitability in the sense that a more shareholder-oriented strategy focuses on narrow financial objectives such as movements in stock prices and short-termism (Sjöberg, 2009). Second, pro-minority shareholder reforms also reduce the role of stable financing through ‘patient capital’ by an extended firms’ use of external credit, making them more vulnerable to credit market volatility. The emergence of a large number of financial innovations (such as securitization) in the 1980s/1990s played a central role in the change in the traditional role of Comparative Economic Studies

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banks as ‘patient capital’ providers.1 As a consequence, ‘as investors press for larger returns, employees faced greater risks (…) wage and employment volatility have risen considerably’ (Jacoby, 2008, p. 18). Second, I argue that the use of pay incentive schemes induced by financial development should be related to higher demand volatility. In this line, Thesmar and Thoenig (2004) demonstrate that financial development, by broadening the pool of external investors both at the domestic and international levels, improves risk-sharing but also encourages firms to adopt more profitable and riskier strategies, and this including for non-listed firms. In this case, firms have higher incentives to introduce specific income schemes such as performance-related pay, individual or collective bonuses for employees (eg, employees stock ownership plan) that are indexed on the firms’ profits, making incomes more volatile (OECD, 2011). In addition, Thesmar and Thoenig (2004) show that financial development has a general equilibrium effect on wages and price and should impact the strategies of non-listed firms. This will result in an overall increase in the uncertainty of sales, employment and profits in all firms. For this reason, more developed financial markets should affect product demand and therefore labor demand. Then, it can be argued that financial development is more particularly related to higher elasticity of labor demand for low-skilled workers. Low-skilled workers should have a higher degree of risk aversion, making them probably more resistant against greater volatility in financial and labor markets. As shown in a recent paper by Pagano and Pica (2012), financial development favors labor reallocation across industries from the ‘weaker’ to the ‘stronger’ industries. As the result, workers can expect higher future labor income, but at the same time income risk can increase because of labor reallocation. Similarly, a large literature has shown that low-skilled workers should be more particularly exposed to higher volatilities on wages and hours worked because labor demand for low-skilled workers is more elastic (Rodrik, 1997; Scheve and Slaughter, 2004). To this regard, Buch and Pierdzioch (2014) have investigated the effect of financial globalization on labor-market volatility across skill levels. They show that financial openness particularly increases volatility of hours worked for low-skilled workers: their empirical results indicate that financial globalization is associated with a rise in output volatility that may trigger more 1

For instance, the traditional compromise between blockholders, managers and trade unions in Germany was undermined when large German banks (such as Deutsche Bank, Dresdner Bank) in the mid-1980s ceased to play their traditional role of ‘patient capital’ providers to benefit from the internationalization of the Anglo-Saxon banks. Gradually, banks sold the shares they held in firms, withdrew from their management and supervisory boards and became investment banks. Obviously these evolutions directly affected the behavior of large German firms, becoming more sensitive from the pressures to generate ‘shareholder value’. Comparative Economic Studies

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additional hires and fires of low-skilled workers because they can less easily absorb productivity shocks compared with high-skilled workers. Then, consumption volatility is also lower for this category of workers because they have easier access to financial markets to smooth their consumption. Hypothesis 1: Financial development is positively correlated to labor-market volatility, especially for low-skilled workers.

Does labor-market regulation matter? All OECD countries have experienced a large increase in their financial activities since the last decades. However, the increase in labor-market volatility has been much more modest in some countries. To explain these cross-country differences in labor-market volatility, I argue that labor-market regulation may influence the direct relationship between financial development and labor-market volatility. It has been well recognized in the literature that specific labor-market regulations (such as unemployment benefits or jobprotection laws) are designed to reduce temporary fluctuations of income (Jetter et al., 2013; Bertola and Lo Prete, 2015). In this context, all these institutions can dampen labor-market volatility, and this more especially for low-skilled workers. More specifically, some labor-market institutions, such as unemployment benefits, can also provide insurance and protect the citizens against major economic risks (unemployment, poverty, sickness, family and so on) and then be negatively correlated to labor-market volatility. A large literature in labor economics has analyzed the effect of labormarket regulation on labor-market volatility. Some theoretical and empirical contributions have shown that stronger labor-market institutions have a reducing effect on labor-market fluctuations. First, it has been shown that strong unions’ bargaining power and employment protection appear as a powerful driver for specific skill investment (Wasmer, 2006) and can dampen labor-market fluctuations as powerful ‘automatic stabilizers’.2 The automatic stabilizers can better absorb the shocks associated with higher economic volatility and instability (Stiglitz, 2000). I use the concept of automatic stabilizers to analyze the reducing effect of social and welfare policies on labor-market volatility. Investments in match-specific human capital reduce the outside option for workers, implying less incentives to separate. Cairo and Cajner (2014) show that low-skilled workers have experienced higher Automatic stabilizers are specific features of government spending to dampen the fluctuations in real GDP. Fatas and Mihov (2001) show that government expenditure through automatic stabilizers has a reducing effect on output volatility. 2

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employment volatility because of higher volatile separation rates. In this vein, the OECD (2011) notes that temporary workers are also much more likely to experience earnings volatility, both within full-time jobs and because of movements into and out of work. Similarly, Sala et al. (2012) show that turnover rates are higher for temporary workers, implying higher employment and wage volatility. Moreover, the lack of the on-the-job training is also a central determinant in explaining labor-market volatility for temporary workers. Second, social and welfare policies have more specifically a strong negative direct effect on labor-market fluctuations. As noted in a report from the OECD (2011), ‘tax and welfare system scan help buffer households against volatile earnings. Taxes play a prominent role in reducing the impact of earnings fluctuations among full-time workers, while transfers such as unemployment benefits and social assistance are more important when volatility is due to movements into or out of work’. Thus, rising volatility may be expected to reduce welfare for risk-averse workers with limited insurance. Welfare and social policies can in this case provide insurance and protect the citizens against major economic risks. In this regard, Rodrik (1997) claims that workers will support social policies that provide social insurance against external risk generated by increased trade openness. Accordingly, trade openness should be positively correlated to public spending in the OECD countries.3 The central point in this paper is to focus on the interactions between financial structures and labor-market institutions (such as employment protection and redistribution). I focus on two main theoretical mechanisms: (1) the use of pay incentive schemes and (2) the incentive for workers to invest in specific human capital, which both are related to labor-market volatility. First, I argue that financial markets and labor-market regulation have a joint impact on the corporate strategies related to firms’ pay strategies that will affect labormarket volatility. According to Thesmar and Thoenig (2004), the degree of financial development and labor-market regulation may jointly influence the firms’ adoption of a riskier corporate strategy. They find that strong labormarket regulation increases the costs of choosing a risky strategy by adopting some variable pay schemes. For instance, powerful trade unions (which play a central role in pay compression) and intermediate or sectoral wage bargaining (which reduces the possibility of the adoption of variable pay schemes depending on individual company performance) seem to lower the benefits of the use of such pay schemes. In other words, firms have higher incentives to use pay incentive schemes and to adopt a riskier strategy, especially when labor markets are more flexible. Accordingly, regulated financial markets 3 Rodrik (1997) finds no empirical evidence of this argument when country-specific characteristics are controlled for fixed-effects.

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combined with more regulated labor markets (which include more generous social and welfare policies) are more likely to reduce the incentives for firms to adopt a riskier corporate strategy (through variable pay schemes) generating higher employment and wage volatility. By contrast, less flexible labor markets imply riskier corporate strategies, which increases volatility when financial development is higher. As a consequence, in the economies with higher (resp. lower) developed financial markets and less (resp. more) regulated labor markets, firms have higher (resp. lower) incentives to adopt riskier corporate strategies that will make workers’ wages and employment more volatile. Labor-market regulation can then efficiently mitigate income and employment risk as powerful automatic stabilizers. Second, institutional interactions between the financial structures and labor-market regulation can also jointly affect the incentive for workers to invest in specific human capital, which can also be related to lower labor-market volatility. For instance, a low degree of financial development (ie, strong capital ownership concentration through insider monitoring) and strong labor market regulation are more likely to promote the development of internal labor markets, which increases the incentives for workers to invest in specific human capital and thus implies lower labor reallocation (Wasmer, 2006), and then to produce a lower labormarket volatility. Hypothesis 2: The positive correlation between financial development and labor market volatility is stronger in countries with weaker labor market regulation.

DATA AND TRENDS Measuring labor-market volatility To obtain a measure of labor-market volatility, I use the EU-KLEMS Database from the OECD that provides data on total hours worked by persons engaged and total labor compensation by skill groups (HS for high-skilled workers and LS for low-skilled workers) from 1974 (for some countries) to 2007. The EU-KLEMS provides data on labor compensation and the number of hours worked by skill levels only in the March 2008 database. Unfortunately, the updated databases do not provide any information on shares in total hours and in total labor compensation. In addition, data on the share of total hours and total labor compensation by skill levels are missing for some countries in many years. Table 1 describes data coverage of my measure of labor-market volatility by country. For most countries, this measure is only available from the early 1980s. Comparative Economic Studies

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Wage volatility

High-skilled

Low-skilled

High-skilled

Low-skilled

Australia Austria Belgium Denmark Finland France Germany Ireland Italy Japan The Netherlands Spain Sweden The United Kingdom The United States

0.0343 0.0333 0.0158 0.0209 0.0254 0.0357 0.0305 0.0526 0.0185 0.0225 0.0434 0.0402 0.0434 0.0473 0.0360

0.0289 0.0184 0.0131 0.0251 0.0284 0.0185 0.0262 0.0530 0.0291 0.0237 0.0568 0.0276 0.0332 0.0425 0.0459

0.0749 0.1046 0.0951 0.0945 0.0904 0.0843 0.0949 0.0863 0.0977 0.0984 0.1240 0.0710 0.0883 0.0768 0.0233

0.0644 0.0906 0.0919 0.0894 0.0835 0.0933 0.0933 0.0795 0.1025 0.0958 0.1004 0.0794 0.0798 0.0824 0.0445

Time (by decade) 1970–1979 1980–1989 1990–1999 2000–2007 Total

0.0373 0.0313 0.0365 0.0280 0.0327

0.0405 0.0248 0.0335 0.0323 0.0311

0.0658 0.0868 0.0975 0.0786 0.0865

0.0623 0.0870 0.0946 0.0771 0.0848

Data coverage

1983–2007 1981–2007 1981–2007 1981–2007 1974–2007 1981–2007 1974–2007 1989–2007 1974–2007 1974–2007 1980–2007 1981–2007 1982–2007 1974–2007 1974–2007

I build two different measures of labor-market volatility by following several steps conducting the same methodology used by Buch and Pierdzioch (2014): 1. First, I calculate the number of hours worked by persons engaged across skill groups (hfit) by computing the total hours worked by persons engaged (H_EMPit) in country i for the year t weighted by the share of hours worked, respectively, by high-skilled and low-skilled persons engaged in total hours (Hfit/100) with f = {HS;LS}). Then, I calculate average hourly wages for each skill level (wfit) by computing labor compensation converted into constant US dollar (LABit) divided by the number of hours worked (H_EMPit) weighted by the share of high-skilled (respectively, low-skilled) labor compensation in total labor compensation (LABfit/100) with f = {HS;LS}).4 2. Then, as it is very usual in the literature (Buch and Pierdzioch, 2014; Jetter et al., 2013), I apply a Hodrick-Prescott (HP) filter that decomposes the series (xt) into a cyclical (yt) and a trend (τt) component. As suggested by 4 Labor compensation converted into constant US dollar is obtained by using the exchange rate series from the Penn World Tables and by deflating by the US output price index.

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Ravn and Uhlig (2002), I use a value of the smoothing parameter λ of 6.25 for annual data. The parameter determines the relative importance of the trend and the cyclical component. 3. Finally, I calculate the growth rate of the cyclical trend of hours worked and of average wages. I compute the rolling standard deviation for growth rates over a 5-year window by skill groups.

Computing volatility over a chosen rolling window is a common measure of volatility in the empirical literature. However, as underlined by Broto et al. (2011), this method has some drawbacks: (1) this method can generate some problems of endogeneity and serial correlation; (2) there is a loss of observations corresponding to the number of years used in the rolling window; and (3) the choice of the rolling window is somewhat arbitrary. As an alternative measure of volatility, Broto et al. (2011) propose the Generalized Autoregressive Conditional Heterogeneity (GARCH) model. However, as noted by Broto et al. (2011), using an unbalanced panel sample can entail serious caveats because of data scarcity (leading to convergence errors). Besides, the GARCH model is based on maximum likelihood estimates that can contain considerable biases for small samples. For these reasons, I decided to compute volatility over a chosen rolling window. Table 1 reveals some differences in the level of volatility of hours worked by skill groups. First, some Anglo-Saxon countries (such as Ireland, the United Kingdom and the United States) have experienced higher volatilities of hours worked for high-skilled and for low-skilled workers. By contrast, some Northern European countries (such as Belgium, Denmark and Finland) share low levels of volatility of hours worked. In addition, it is striking that in some countries (ie, Ireland, Italy, the Netherlands and the United States) low-skilled workers have been particularly confronted with increasing volatility compared with high-skilled workers. As regards the volatility of wages, descriptive statistics indicate some differences in employment volatility: very surprisingly, one can see that some countries with low employment volatility (Austria, Belgium, Germany and Japan) have higher levels of wage volatility. By contrast, Ireland, the United Kingdom and the United States are characterized by lower volatility of wages. Finally, some countries (such as France, Italy, Spain, the United Kingdom and the United States) present higher levels for low-skilled workers than high-skilled workers. On average, I find, as in Buch and Pierdzioch (2014), that the volatility of hours worked has been higher for high-skilled workers than for low-skilled workers. The main objective of this paper is to explain cross-national differences in labor-market volatility, and this across different skill levels. Comparative Economic Studies

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Measures of financial development and labor-market regulation Measuring financial development The main objective of this paper is to examine the relationship between financial development and labor-market volatility in the OECD countries. My main argument is based on the idea that the development of financial markets should be related to corporate strategies and finally to how firms will determine wages and the number of hours worked. This argument would require to isolate the component of financial development that is related to a change in corporate governance. Unfortunately, no such variable is currently available in a time-series cross-sectional analysis in the OECD countries. Instead, I use two overall measures of financial development: the stock market capitalization ratio and the employment share in the financial sector.5 I use as a first financial variable the stock market capitalization ratio as a percentage of GDP. This variable is provided by the Financial Structure Database from the World Bank (Beck et al., 2010). This first variable gives me information on the size of stock markets as a share of GDP. More developed stock markets are supposed to increase the investors’ opportunities for risk diversification. Most Anglo-Saxon countries (such as Australia, the United Kingdom and the United States) have higher stock market capitalization ratio. Anglo-Saxon countries traditionally have well-developed stock markets that have been deregulated in the late 1970s or in the early 1980s. Stock markets were deregulated in other countries in the late 1980s and grew more particularly during the 1990s. As argued in the section ‘Conceptual framework’, financial development is not limited to stock market development but can include some recent evolutions in the banking structures. To capture the importance of the financial markets and financial institutions in the whole economy, I refer to the concept of ‘financialization’. I use one aggregate indicator measuring the extent of financialization in a time-series cross-sectional analysis: the share of employment in finance in the total employment. Shares are computed by using the EU-KLEMS Database from the OECD that provides data on employment across sectors based on national accounts from 1970 to 2007. The financial sector here refers to ‘financial intermediation’ that includes financial intermediation, except insurance and pension funding, insurance and pension funding, except compulsory social security and activities auxiliary to financial intermediation following the NACE classification. 5 I use in robustness checks the share of domestic credit to private sector by banks in the GDP as another common measure of financial sector depth.

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Measurement of labor-market regulation According to Checchi and Garcia-Penalosa (2008), labor-market institutions encompass different aspects of government policies to employee organizations, such as the employment protection legislation, the union density and coverage, the degree of centralization or coordination of wage bargaining. Labor-market institutions also refer to unemployment benefits and more broadly to redistribution policies. These two dimensions of labor-market regulation are taken into account in this paper. First, I use the trade-union density rate to measure the first dimension. This variable is calculated by the OECD as the proportion of union members among workers. Data are available from 1980 to 2007 and are provided by the OECD.6 Then, I use alternative measures for the second dimension that refers to the protection of the citizens against major economic risks in the areas of unemployment, poverty, sickness, family, active labor-market programs, housing and so on. In this regard, I use three different variables: (1) the public social expenditure as a percentage of GDP; (2) an overall index of welfare generosity; and (3) a measure of redistribution. First, I use public social expenditure as a percentage of GDP that covers all public expenditure, including old age, survivors’ incapacity, health, family, active labor-market programs, unemployment and housing. This variable is provided by the OECD. Then, I use an overall index of welfare generosity. This aggregate index provided by the Comparative Welfare Entitlement Database is a computation of the net replacement rates of unemployment benefits, sickness benefits and pension insurance, the extent of program coverage and its duration (Scruggs et al., 2014).7 Alternatively, I use an index of redistribution. This index provided by the Standardized World Income Inequality Database (Solt, 2009) estimates redistribution by the percentage reduction in gross income inequality, that is, the difference between the market and net income inequality, divided by market income inequality, multiplied by 100. Whereas the share of public social expenditure in GDP measures welfare effort, that is, how much of it is spent on social policies, the index of welfare generosity reflects a measure of eligibility and generosity. 6

Other common measures of labor market regulation, such as the strictness of employment protection legislation and the degree of coordination in wage bargaining, have been used in the analysis. But, the threshold regression method used in the empirical analysis in the section ‘Estimation results’ indicates that these two different variables cannot be used to sample-split. I fail to reject the null hypothesis of no threshold H0:β1 = β2. 7 Unemployment and sickness generosity indexes are calculated on replacement rate, qualification period, duration, waiting days and coverage. Pension generosity index is calculated on replacement rate, expected pension duration years, pension qualification years and employee pension funding ratio. Comparative Economic Studies

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Similarly, redistribution measures how social policies are successful in reducing income inequality and reflects welfare outcome.

EMPIRICAL STRATEGY Panel data analysis of financial development on labor-market volatility First, I run fixed-effect estimations to analyze the direct effect of higher financial development on employment and wage volatility. I will estimate the following relationship to test my first hypothesis (Hypothesis 1): Yit ¼ β1  FINit + βk  Xk;it + λi + ηt + εit

(1)

where Yit denotes my measures of labor-market volatility for each skill group. FINit is a set of two variables capturing financial development (stock market development and employment share in the financial sector), and Xk,it is a vector of time-varying country controls. The specification includes a set of country fixed-effects (λi) and year fixed-effects (ηt). εit is a disturbance term. I first test for the pooling restrictions: if parameters of equation 1 are equal across countries, time-series and cross-sectional data is more appropriate in this case. I run a Breusch–Pagan test. The null hypothesis in this test is that variances across entities are zero. This test implies that equation 1 includes country individual effects and shows strong evidence of significant differences across countries. I decided to run fixed-effects estimations as suggested by a Hausman test. My choice of control variables is in line with the existing literature (Buch and Pierdzioch, 2014). First, I control for unemployment rates (Unemployment rate) as a determinant of labor-market volatility, in particularly for low-skilled workers. Low-skilled workers are more likely to have a higher risk of becoming unemployed or jobless and with more persistent periods of unemployment, then affecting their labor-market volatility. A positive relationship between higher unemployment rates and my two measures of labor-market volatility is expected, and this especially for low-skilled workers. Then, I control for the volatility of total factor productivity (TFP): variations in TFP could be important in explaining the observed labor-market volatility. More specifically, it is very usual to consider differences in employment and wages across skill levels by the differences in productivity level. Volatility of TFP should increase in output and in hours worked, especially for high-skilled workers, because of lower costs adjusting hours worked per labor compensation (Buch and Pierdzioch, 2014). Accordingly, higher TFP volatility should be positively correlated to my dependent variables. The EU-KLEMS Database provides information on TFP growth, value-added based. I compute the rolling standard Comparative Economic Studies

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deviation for TFP growth over a 5-year window (Volatility of TFP). Finally, I also control for output volatility that is expected to impact labor market and then to increase their volatility. I calculate the volatility of output growth by computing the rolling standard deviation for growth rates of real GDP over a 5-year window (Output volatility). Panel threshold regression analysis of financial development on labormarket volatility Then, I want to analyze how financial structures may interact with labormarket regulation to explain cross-country differences in labor-market volatility. To capture the interdependences between financial and labor-market variables, the use of an interaction term is very common. But this method raises two different problems. First, the linkages between financial development, labor-market variables and labor-market volatility are very complex to analyze. Financial development may depend on household demand for private insurance, which in turn depends on the availability of social insurance. Therefore, it is very difficult to disentangle all these variables. Second, introducing a misspecified interaction term between financial and labormarket variables may produce biased coefficients because of potential multicollinearity (Chatelain and Ralf, 2014). To avoid these two different problems, I use a sample-split (depending on labor-market institutional features) to account for different skill levels with respect to Pagano and Pica (2012). Hansen (1999) proposes a sample-split and threshold regression technique appropriate for panel data analysis. In other words, I suspect a non-linear relationship between my financial variables and my measures of labor-market volatility. I argue in the section ‘Conceptual framework’ that the effect of financial development on labor market should be impacted by labor-market institutions. More particularly, I assume that the increasing effect of financial development should be undermined by the reducing effect of labor-market regulation. In line with my second hypothesis (Hypothesis 2), I decide to split my sample depending on specific labor-market institutional features, expressed as follows from equation 1: ( 0 Yit ¼ β1  FINit + βk  Xk;it + λi + ηt + εit if LABit ≤ γ yit ¼ : (2) 0 Yit ¼ β2  FINit + βk  Xk;it + λi + ηt + εit if LABit >γ with LABit denoting labor-market variables. These variables are used in my analysis as threshold variables in order to sort the data in different regimes or groups of countries. The threshold model permits the regression parameters 0 0 (β1 and β2 ) to switch between regimes depending on whether LABit is 0 0 smaller or larger than the threshold value γ. Hypothesis 2 implies that β1 >β2 . Comparative Economic Studies

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Equation 2 can be rewritten as in a single equation form with the introduction of the indicator function I(⋅): 0

0

Yit ¼ β1  FINit  IðLABit ≤ γÞ + β2  FINit  IðLABit >γÞ + βk  Xk;it + λi + ηt + εit (3) This method has several advantages. First, this model endogenously identifies the thresholds for LAB at which the relationship between labormarket volatility and financial development switches. In other words, the model searches over all values of LABit for γ sequentially until sample-splitting value ^γ is found. The procedure proposed by Hansen (1999) also allows for testing the statistical significance of the threshold effect (ie, the null hypothesis of no threshold H0:β1 = β2 against the alternative hypothesis of having at least one threshold H1:β1≠β2). To do this, Hansen (1999) recommends a bootstrap procedure to determine critical values of the test statistics. The critical values for determining the 95% confidence interval of the threshold values are given by: Γ ¼ fγ : LRðγÞ ≤ CðαÞg

(4)

where C(α) is the 95% percentile of the asymptotic distribution of the likelihood ratio (LR(γ)). The null hypothesis of no threshold effect will be rejected if the bootstrap estimate of the p-value for the likelihood ratio (LR) test is smaller than the desired critical values (eg, 5%). Then, once a threshold is found, Hansen (1999) recommends to test the presence of two thresholds with the help of another LR test. Second, as earlier mentioned, this method deals with the multicollinearity problem raised by the use of misspecified interaction terms (Chatelain and Ralf, 2014). A potential problem with this approach to identifying the thresholds is that the welfare state and labor-market variables are potentially endogenous while the Hansen (1999) method requires that independent variables are strictly exogenous. Labor-market institutions, and more particularly social and welfare policies, can be endogenous to economic conditions: as noted by Rodrik (1998) and Fatas and Mihov (2001), governments should be capable of stabilizing labor-market fluctuations if such economies are more volatile. In order to take into account the effect of labor-market volatility on my different labor-market variables, and hence to address this issue of potential reversed causality, I run IV-2SLS regressions (producing autocorrelation-robust covariance matrix and standard errors) and implement several endogeneity tests for the potential endogenous variables. I fail to reject the null and conclude that all my labormarket variables are actually exogenous. Comparative Economic Studies

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ESTIMATION RESULTS Is financial development positively correlated to labor-market volatility? At this first stage of the analysis, my sample is composed of 15 OECD countries (Australia, Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Spain, Sweden, the United Kingdom and the United States) from 1974 to 2007 when using the employment share in the financial sector as a financial variable and restricting from 1989 to 2007 when using the stock market capitalization ratio. Table A1 in Appendix describes all the variables used in my regression analysis. Fixed-effects regression results showing the impact of stock market development and employment share in the financial sector are displayed in Table 2. I estimate equation 1 separately for each skill group (high-skilled and low-skilled workers). Columns (1) and (2) show results when using the volatility of hours worked as dependent variable, whereas columns (3) and (4) present results when using the volatility of wages as dependent variable. Table 2A presents the results of the impact of stock market development on labormarket volatility. In Table 2B, I use the employment share in the financial sector as an alternative measure of financial development.8 To start with, I find that some of my control variables have the expected sign. First, I find contrasted evidence of a positive effect of unemployment on labor-market volatility. Results show that higher unemployment rates are associated with higher wage volatility with a larger effect for high-skilled workers. I find that higher unemployment is also negatively correlated to employment volatility for high-skilled workers, suggesting that high-skilled workers can more easily absorb productivity shocks. Second, when statistically significant, I find that TFP and output volatilities are, as expected, associated with higher labor-market volatility. Volatility of TFP should increase in output and in hours worked, especially for high-skilled workers, because of lower costs adjusting hours worked per labor compensation (Buch and Pierdzioch, 2014). I find that volatility of TFP exerts a positive effect on employment volatility but only when considering the employment share in the financial sector as a financial variable (Table 2B) with a larger effect for high-skilled workers. Finally, higher output volatility is more likely to affect low-skilled workers than high-skilled workers (models (2) and (4) in Table 2A). When focusing on my two different financial variables, I find that higher stock market development has no significant impact on labor-market volatility (Table 2A), F-tests have been run to test the presence of year fixed-effects. The null hypothesis assumes that all year coefficients are equal to zero. If I fail to reject the null, no time fixed-effects are needed only when considering employment volatility as dependent variable. 8

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270 Table 2: Fixed-effects estimations Employment volatility

Wage volatility

HS

LS

HS

LS

(1)

(2)

(3)

(4)

0.0087 (0.0056) −0.0014* (0.0007) −0.0025 (0.0021) 0.0086*** (0.0027) 0.0196** (0.0087) 237 15 0.0535 No

−0.0062 (0.0082) 0.0035*** (0.0009) 0.0045* (0.0024) 0.0016 (0.0032) 0.1225*** (0.0109) 237 15 0.5432 Yes

−0.0089 (0.0095) 0.0020** (0.0010) 0.0061** (0.0028) 0.0076** (0.0038) 0.1225*** (0.0128) 237 15 0.4438 Yes

B. Financial variable: Employment share in the financial sector Employment share in finance 0.0169*** 0.0152*** (0.0032) (0.0045) Unemployment rate −0.0011*** −0.0006 (0.0004) (0.0005) TFP volatility 0.0031*** 0.0004 (0.0011) (0.0015) Output volatility 0.0018 0.0016 (0.0011) (0.0015) Constant −0.0299*** −0.0244* (0.0103) (0.0142) Observations 358 358 Number of country 15 15 0.0902 0.0336 R2 Year fixed-effect No No

0.0159* (0.0082) 0.0030*** (0.0009) 0.0096*** (0.0028) −0.0035 (0.0027) 0.0039 (0.0262) 358 15 0.0887 Yes

0.0202** (0.0086) 0.0022** (0.0009) 0.0083*** (0.0029) −0.0014 (0.0028) −0.0016 (0.0272) 358 15 0.0668 Yes

A. Financial variable: Stock market capitalization/GDP Stock market capitalization/GDP 0.0012 (0.0040) Unemployment rate −0.0011** (0.0005) TFP volatility 0.0016 (0.0015) Output volatility 0.0037* (0.0020) Constant 0.0235*** (0.0062) Observations 237 Number of country 15 0.0355 R2 Year fixed-effect No

*P<0.1; **P<0.05; ***P<0.01. Note: Standard errors in parentheses. HS = high-skilled workers and LS = low-skilled workers.

whereas higher employment share in the financial sector is strongly associated with increased labor-market volatility (Table 2B).9 Second, when I compare the effect across skill levels, I find a larger effect on employment volatility for high-skilled workers and a larger effect on wage volatility I find very similar and robust results when calculating my measures of labor market volatility with different values of λ (such as λ = 100). Using alternative filters such as the Baxter–King filter, gives substantially similar results. 9

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for low-skilled workers. In order to compare the respective effect of employment share in the financial sector on labor-market volatility across skill groups, I compute a Wald test of coefficient equality, which suggests that the difference across skill groups is not statistically significant.10 To conclude, Hypothesis 1 is partially validated: I find evidence that higher employment in finance is positively correlated to increased labor-market volatility. Results do not, however, show any significant differences across skill levels. Do labor-market institutions influence the labor-market volatility effect of financial development? In order to test my second hypothesis assuming a non-linear relationship between financial development and labor-market volatility depending on labor-market institutional features, I use a fixed-effect threshold regression model proposed by Hansen (1999). This model, however, requires a strongly balanced panel, restricting my sample to 13 OECD countries (excluding Ireland and Sweden) from 1986 to 2005 when using the employment share in finance and from 1989 to 2005 when using the stock market capitalization ratio. Fixed-effect threshold regression results are displayed in Tables 3 and 4. The estimated threshold values (^γ 1 and ^γ 2 ) for each test are also reported. For each labor-market variable, I test for a single and a double threshold where a number of 300 bootstrap replications were used for each of two bootstrap LR tests.11 I only report the results for the variables where the null hypothesis of no threshold H0:β1 = β2 is rejected (at the 1% and 5% levels of significance). For most specifications, I estimate a panel data model with one single threshold. In specification (6) in Table 4, the LR test suggests that there are two different threshold values. I estimate in this case a panel data with double threshold. Fixed-effect threshold regression results showing the impact of stock market development are displayed in Table 3. Table 4 reports the results when using the employment share in the financial sector as an alternative financial variable. When considering stock market capitalization ratio as a financial measure in Table 3, I find in most specifications strong evidence that the positive correlation between labor-market volatility and financial development is stronger in countries with weaker labor-market regulation, supporting Hypothesis 2 0 0 where β1 >β2 . In other words, I find a larger effect of financial development on labor-market volatility in models (3) and (4) for employment volatility and in 10 11

Results of this test are not here reported. Using more bootstrap replications gives very similar results. Comparative Economic Studies

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Employment volatility HS

HS

LS

LS

LS

LS

HS

LS

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Single threshold Stock market capitalization (trade union≤^γ 1 )

0.0356*** (0.0060) −0.0038 (0.0054)

Stock market capitalization (trade union>^γ 1 ) Stock market capitalization (social security expenditure≤^γ 1 )

0.0433*** (0.0061) −0.0003 (0.0051)

Stock market capitalization (social security expenditure>^γ 1 ) Stock market capitalization (welfare generosity≤^γ 1 ) Stock market capitalization (welfare generosity>^γ 1 ) Stock market capitalization (redistribution≤^γ 1 )

Wage volatility

−0.0071* (0.0038) 0.0301*** (0.0050)

Stock market capitalization (redistribution>^γ 1 )

−0.0202*** (0.0051) 0.0104*** (0.0039)

−0.0019 (0.0048) 0.0641*** (0.0069)

Double threshold Stock market capitalization (redistribution≤^γ 1 ) Stock market capitalization (^γ 1 < redistribution≤ ^γ 2 ) Stock market capitalization (^γ 2
36.90 Yes 221 0.2535 13

34.64 Yes 221 0.2021 13

23.75 Yes 221 0.2697 13

11.69 Yes 221 0.3218 13

37.00 Yes 221 0.3842 13

0.0206 0.0288** (0.0130) (0.0144) −0.0540*** −0.0510*** (0.0077) (0.0086)

−0.0082 (0.0063) 0.0624*** (0.0074) 0.0081 (0.0056) 34.98 39.50 Yes 221 0.3914 13

24.00 Yes 221 0.3130 13

*P<0.1, **P<0.05, ***P<0.01. Note: Standard errors in parentheses. HS = high-skilled workers and LS = low-skilled workers. Financial variable: Stock market capitalization/GDP.

24.00 Yes 221 0.2478 13

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Table 3: Fixed-effects threshold estimations

Table 4: Fixed-effects threshold estimations Employment volatility

Single threshold Employment in finance (trade union ≤ ^γ 1 ) Employment in finance (trade union>^γ 1 )

HS

HS

HS

LS

LS

LS

HS

LS

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0543*** (0.0110) 0.0380*** (0.0111) 33.19 Yes 260 0.2935 13

0.0438*** (0.0113) 0.0261** (0.0115) 31.94 Yes 260 0.2448 13

0.0376*** (0.0047) 0.0296*** (0.0044)

0.0279*** (0.0068) 0.0157** (0.0064) 0.0304*** (0.0046) 0.0222*** (0.0046)

Employment in finance (social security expenditure>^γ 1 ) Employment in finance (welfare generosity ≤ ^γ 1 )

0.0191*** (0.0063) 0.0027 (0.0063) 0.0218*** (0.0046) 0.0287*** (0.0046)

Employment in finance (welfare generosity>^γ 1 ) Employment in finance (redistribution ≤ ^γ 1 )

0.0025 (0.0065) 0.0152** (0.0065)

Employment in finance (redistribution>^γ 1 ) 23.75 Yes 260 0.2869 13

12.31 Yes 260 0.2333 13

37.00 Yes 260 0.2254 13

23.75 Yes 260 0.2219 13

12.31 Yes 260 0.2444 13

37.00 Yes 260 0.1950 13

*P<0.1, **P<0.05, ***P<0.01. Note: Standard errors in parentheses. HS = high-skilled workers and LS = low-skilled workers. Financial variable: Employment share in the financial sector.

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^γ 1 Controls Observations R2 Number of country

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Employment in finance (social security expenditure ≤ ^γ 1 )

Wage volatility

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models (7) and (8) for wage volatility for low values of labor-market variables.12 By contrast, I find in some specifications a larger effect for higher values of labor-market regulation (models (1), (2), (5) and (6)), refuting Hypothesis 2. What this finding could suggest is that labor-market institutions may be detrimental to employment, especially for high-skilled workers. According to Buch and Schlotter (2011), employment volatility could then increase with the level of labor-market regulation by affecting adjustments of the labor force, especially for high-skilled workers. As a consequence, higher labor-market regulation could be associated with an increase in the elasticity of labor demand and could positively affect the volatility of employment. Then, I also find some evidence of Hypothesis 2 in most specifications in Table 4 when using the employment share in the financial sector as the explanatory variable. In other words, I find that the positive correlation between financial development and labor-market volatility is stronger in countries with weaker labor-market regulation. Surprisingly, Hypothesis 2 is, however, not validated when using the degree of overall welfare generosity as a measure of labor-market regulation (models (3) and (6)) where I find a larger effect of financial development on the dependent variables for higher values of overall welfare generosity. To conclude, I find some evidence supporting my two hypotheses: (i) financial development, when measured by higher employment share in the financial sector, is strongly associated with increased labor-market volatility, but with no statistical differences across skill levels and (ii) this positive effect is larger particularly in countries with weaker labor-market regulation. Results are particularly robust when using the employment share in the financial sector as financial indicator. Robustness checks In this subsection, I use an alternative measure of financial sector depth: the share of domestic credit to private sector by banks in GDP, which refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, which establish a claim for repayment. A large literature has 12

Models (7) and (8) indicae that higher stock market development is associated with a reduction (and not an increase as expected) in labor-market volatility for higher values of welfare generosity. Some recent contributions have shown that labor market institutions have a reducing effect on the volatility of real wage growth (Macit, 2010; Buch and Schlotter, 2011). Strong labor market regulation allows to maintain workers’ wages and lead to smoother responses in real wages. Accordingly, the increasing effect of financial development on wage volatility is undermined by the reducing effect of welfare state and labor market variables, including for high-skilled workers. Comparative Economic Studies

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emphasized the role of financial intermediation in instability. For instance, Stiglitz (2000) argues that financial deregulation and the extension of the banking activities have not contributed to the reduction in the asymmetrical information problem and thus have participated to the increase in the differences in credit availability across households. In the same line, Greenwood and Jovanovic (1990) argue that the use of financial intermediation does not hamper poor but favor rich people. In comparison with Table 2, I find very similar fixed-effect estimation results: higher banking credit ratio is positively and significantly associated with higher labor-market volatility only for low-skilled workers. Then, I find similar and robust results on a positive correlation between financial development (measured by banking credit) and labor-market volatility, and this conditional on specific levels of different dimensions of labor-market regulation, validating Hypothesis 2 in most specifications.

CONCLUSION AND POLICY IMPLICATIONS The aim of the paper was to assess how financial development is positively correlated to higher volatility on average wages and employment over the period of 1974–2007 in 15 advanced OECD countries. The originality of this paper is to explain cross-country differences in labor-market volatility using a threshold regression model in panel data proposed by Hansen (1999). First, my analysis on panel data suggests that the increasing influence of the financial markets is positively correlated to higher labor-market volatility. Despite higher possibilities of risk-diversification, higher financial development may be related to a change in firms’ strategies and modify how firms will determine wages and hours worked. Using different overall measures of financial development (which are, however, not specifically related to a change in corporate governance), fixed-effect regression estimations indicate that higher employment share in the financial sector is significantly correlated to my two dependent variables. In addition, results show no significant differences across skill levels, whereas I expected a larger effect for low-skilled workers than for high-skilled workers. Second, using a threshold regression model in panel data on a reduced sample, I find some evidence that this positive correlation between financial development and labor-market volatility is higher (resp. lower) in countries with weaker (resp. stronger) labor-market regulation (ie, the level of government intervention in the labor market and the degree of generosity of social and welfare policies). Results are more robust when explaining employment volatility for low-skilled workers and when explaining wage volatility for all categories of workers. Comparative Economic Studies

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These interactions between financial development and labor-market regulation can be useful to explain cross-national variations in labor-market volatility. As a result, the increase in employment and wage volatility has been more modest in countries with more regulated labor markets. This result has strong policy implications at least in the short run. Whereas a large literature has shown that higher economic growth implies larger macroeconomic volatility, this paper stresses the consequences of financial development on labor-market volatility in the short run. In this regard, less government intervention is associated with higher economic growth in the long run (eg, Erauskin, 2011). However, labor-market regulation through automatic stabilizers is more likely to play an active role in cushioning the effects of the financial markets on labor-market volatility in the short run. This is indeed widespread concern about higher economic uncertainty linked to employment and wage volatility. From this perspective, faced with higher insecurity, workers could support higher redistribution (Rodrik, 1997).

Acknowledgements The author wants to thank Bruno Amable, Karim Azizi, Christophe Rault, Antoine Rebérioux, all participants of the 25th Annual Conference of the Society for the Advancement of Socio-Economics (SASE) at the University of Milan, 27–29 June 2013, two anonymous referees and the Editor for valuable comments on a previous version. REFERENCES Beck, T, Asli Demirgüç-Kunt, A and Levine, R. 2010: A new database on financial development and structure. World Bank, http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/ 0,,contentMDK:20696167~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html, accessed 25 January 2016. Bertola, G and Lo Prete, A. 2015: Labor market reforms, finance, and the current account. Review of International Economics 23(3): 469–488. Broto, C, Diaz-Cassou, J and Erce, A. 2011: Measuring and explaining the volatility of capital flows to emerging countries. Journal of Banking and Finance 35(8): 1941–1953. Buch, CM and Schlotter, M. 2011: Regional origins of employment volatility: Evidence from German states. Empirica 40(1): 1–19. Buch, CM and Pierdzioch, C. 2014: Labor market volatility, skills, and financial globalization. Macroeconomic Dynamics 18(5): 1018–1047. Cairo, I and Cajner, T. 2014: Human Capital and Unemployment Dynamics: Why More Educated Workers Enjoy Greater Employment Stability, Finance and Economics Discussion Series, Federal Reserve Board, 2014–09. Chatelain, JB and Ralf, K. 2014: Spurious regressions and near-multicollinearity, with an application to aid, policies and growth. Journal of Macroeconomics 39(A): 85–96. Checchi, D and Garcia-Penalosa, C. 2008: Labour market institutions and income inequality. Economic Policy 23(56): 600–651. Comparative Economic Studies

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277 Darcillon, T. 2015: Corporate governance reforms and political partisanship: An Empirical Analysis in 16 OECD Countries. Business and Politics 17(4): 661–676. Erauskin, I. 2011: Financial openness, volatility, and the size of productive government. SERIEs 2(2): 233–253. Fatas, A and Mihov, I. 2001: Government size and automatic stabilizers: International and intranational evidence. Journal of International Economics 55(1): 3–28. Gourevitch, PA and Shinn, J. 2005: Political power and corporate control: The new global politics of corporate governance. Princeton University Press: Princeton. Greenwood, J and Jovanovic, B. 1990: Financial development, growth, and the distribution of income. Journal of Political Economy 98(5): 1076–1107. Hansen, B. 1999: Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics 93(2): 345–368. Jacoby, SM. 2008: Finance and labor: Perspectives on risk, inequality, and democracy. Comparative Labor Law & Policy Journal 30(1): 17–65. Jetter, M, Nikolsko-Rzhevskyy, A and Smith, WT. 2013: The effects of wage volatility on growth. Journal of Macroeconomics 37(1): 93–109. Macit, F. 2010: The role of labor market institutions on wage and inflation dynamics: Empirical evidence from OECD economies. Economic Analysis & Policy 40(1): 49–62. OECD. 2007: OECD workers in the global economy: Increasingly vulnerable? OECD employment outlook 2007. OECD Publishing: Paris. OECD. 2011: Earnings volatility: Causes and consequences. OECD employment outlook 2011. OECD Publishing: Paris. Pagano, M and Pica, G. 2012: Finance and employment. Economic Policy 27(69): 5–55. Pagano, M and Volpin, P. 2005: The political economy of corporate governance. American Economic Review 95(4): 1005–1030. Ravn, MO and Uhlig, H. 2002: On adjusting the Hodrick-Prescott filter for the frequency of observations. The Review of Economics and Statistics 84(2): 371–380. Rodrik, D. 1997: Has globalisation gone too far? Institute for International Economics: Washington DC. Rodrik, D. 1998: Why do more open economies have bigger governments? Journal of Political Economy 106(5): 997–1032. Roe, MJ. 2003: Political determinants of corporate governance. Political context, corporate impact. Oxford University Press: Oxford. Sala, H, Silva, JI and Toledo, M. 2012: Flexibility at the margin and labor market volatility in OECD countries. Scandinavian Journal of Economics 114(3): 991–1017. Scheve, K and Slaughter, MJ. 2004: Economic insecurity and the globalization of production. American Journal of Political Science 48(4): 662–674. Scruggs, L, Detlef, J and Kati, K. 2014: Comparative welfare entitlements dataset 2. Version 2014–03. University of Connecticut & University of Greifswald, http://cwed2.org/, accessed 25 January 2016. Sjöberg, O. 2009: Corporate governance and earnings inequality in the OECD countries 1979–2000. European Sociological Review 25(5): 519–533. Solt, F. 2009: Standardizing the world income inequality database. Social Science Quarterly 90(2): 231–242. Stiglitz, JE. 2000: Capital market liberalization, economic growth, and instability. World Development 28(6): 1075–1086. Thesmar, D and Thoenig, M. 2004: Financial market developments and the rise in firm level uncertainty. Centre for Economic Policy Research: London, http://www.cepr.org/active/publications/ discussion_papers/dp.php?dpno=4761, accessed 25 January 2016. Wasmer, E. 2006: Interpreting Europe-US labour market differences: The specificity of human capital investments. American Economic Review 96(3): 811–831.

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Table A1: Variables description Variables Dependent variables Employment volatility Wage volatility Financial variables Stock market development Employment share in the financial sector Banking credit Welfare state and labor-market variables Trade union density Social security expenditure Overall welfare generosity index Redistribution Control variables Unemployment rate TFP volatility Output volatility

Description

Time

N

Rolling standard deviation for growth rates of the number of hours worked by persons engaged over a 5-year window for high-skilled and low-skilled workers (Source: EU-KLEMS Database) Rolling standard deviation for growth rates of the average wage over a 5-year window for high-skilled and low-skilled workers (Source: EU-KLEMS Database)

1974–2007

437

1974–2007

437

Stock market capitalization to GDP (Source: Financial Structure Database) Share of employment in the financial sector in the total employment (Source: EU-KLEMS) Domestic credit to private sector by banks to GDP (Source: Financial Structure Database)

1989–2007 1970–2007 1970–2007

278 567 567

Proportion of union members among workers (Source: OECD) Public social expenditures as a percentage of GDP (old age, survivors’ incapacity, health, family, active labor-market programs, unemployment and housing) from 1970 to 2007 (Source: OECD Social Expenditure Database (SOCX)) Composite index calculated by the sum of subindexes of unemployment, sickness and pension generosity (Source: Comparative Welfare Entitlement Database) Difference between the pre-tax and post-tax income inequality, divided by pre-tax income inequality, multiplied by 100) as an estimate of redistribution (Source: Solt, 2009)

1970–2007 1970–2007

570 570

1971–2007

525

Standardized unemployment rates (Source: OECD Main Economic Indicators Database) Rolling standard deviation for TFP growth rates (value-added based) over a 5-year window (Source: EU-KLEMS) Rolling standard deviation for real GDP growth rates over a 5-year window (Source: OECD Economic Outlook Database)

1970–2007 1970–2007

570 418

1974–2007

510

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APPENDIX